SDC-System-Integration Generative Adversarial Network (GAN) Classifier¶
In [1]:
# import some useful modules
%matplotlib inline
import pickle as pkl
import time
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
import pandas as pd
import cv2
from os.path import isfile, isdir
from tqdm import tqdm
from IPython.display import display
Import our data set (Just the Simulator)¶
In [2]:
incsv1 = '../../data/collections/samples/session1.csv'
incsv2 = '../../test_images/loop_with_traffic_light.csv'
incsv3 = '../../test_images/just_traffic_light.csv'
sample1 = pd.read_csv(incsv1, header=0, sep=',')
sample1 = sample1.drop('x', axis=1)
sample1 = sample1.drop('y', axis=1)
sample1 = sample1.drop('z', axis=1)
sample1 = sample1.drop('ax', axis=1)
sample1 = sample1.drop('ay', axis=1)
sample1 = sample1.drop('az', axis=1)
sample1 = sample1.drop('aw', axis=1)
sample2 = pd.read_csv(incsv2, header=0, sep=',')
sample2 = sample2.drop('x', axis=1)
sample2 = sample2.drop('y', axis=1)
sample2 = sample2.drop('z', axis=1)
sample2 = sample2.drop('ax', axis=1)
sample2 = sample2.drop('ay', axis=1)
sample2 = sample2.drop('az', axis=1)
sample2 = sample2.drop('aw', axis=1)
sample3 = pd.read_csv(incsv3, header=0, sep=',')
sample3 = sample3.drop('x', axis=1)
sample3 = sample3.drop('y', axis=1)
sample3 = sample3.drop('z', axis=1)
sample3 = sample3.drop('ax', axis=1)
sample3 = sample3.drop('ay', axis=1)
sample3 = sample3.drop('az', axis=1)
sample3 = sample3.drop('aw', axis=1)
#frames = [ sample1, sample2, sample3 ]
frames = [ sample1 ]
samples = pd.concat(frames)
samples.columns = ['X','y']
samples.head()
Out[2]:
In [3]:
samples.describe()
Out[3]:
In [4]:
# number of samples
n_samples = len(samples.index)
# calculate the number of RED light: 0
n_red = len(samples[(samples.y==0)])
# calculate the number of YELLOW light: 1
n_yellow = len(samples[(samples.y==1)])
# calculate the number of GREEN light: 2
n_green = len(samples[(samples.y==2)])
# calculate the number of UNKNOWN: 4
n_unknown = len(samples[(samples.y==4)])
# Print the results
print "Total number of samples: {}".format(n_samples)
print "Number of samples red: {}".format(n_red)
print "Number of samples green: {}".format(n_green)
print "Number of samples yellow: {}".format(n_yellow)
print "Number of samples unknown: {}".format(n_unknown)
In [5]:
msk = np.random.rand(len(samples)) < 0.8
trainset = samples[msk]
testset = samples[~msk]
# Show the results of the split
print "Training set has {} samples.".format(trainset.shape[0])
print "Testing set has {} samples.".format(testset.shape[0])
print trainset.head()
print testset.head()
In [6]:
def getImage(imagefile):
imagepath = '../../'+imagefile.replace("'", "")
image = cv2.imread(imagepath, 1)
row, col, ch = image.shape
if row != 600 or col != 800:
image = cv2.resize(image, (600, 800), interpolation=cv2.INTER_AREA)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
In [7]:
idx = np.random.randint(0, len(trainset['X']), size=36)
fig, axes = plt.subplots(6, 6, sharex=True, sharey=True, figsize=(16,12),)
for ii, ax in zip(idx, axes.flatten()):
ax.imshow(getImage(trainset['X'].iloc[ii]), aspect='equal')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
plt.subplots_adjust(wspace=0, hspace=0)
Preparing the data¶
In [8]:
def scale(x, feature_range=(-1, 1)):
# scale to (0, 1)
x = ((x - x.min())/(255 - x.min()))
# scale to feature_range
min, max = feature_range
x = x * (max - min) + min
return x
In [9]:
class Dataset:
def __init__(self, train, test, val_frac=0.5, shuffle=False, scale_func=None):
split_idx = int(len(test['y'])*(1 - val_frac))
self.test_x, self.valid_x = test['X'][:split_idx], test['X'][split_idx:]
self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:]
self.train_x, self.train_y = train['X'], train['y']
self.label_mask = np.ones(len(self.train_y)+128)
if scale_func is None:
self.scaler = scale
else:
self.scaler = scale_func
self.shuffle = shuffle
def batches(self, batch_size, which_set="train"):
x_name = which_set + "_x"
y_name = which_set + "_y"
num_examples = len(getattr(dataset, y_name))
if self.shuffle:
idx = np.arange(num_examples)
np.random.shuffle(idx)
setattr(dataset, x_name, getattr(dataset, x_name)[idx])
setattr(dataset, y_name, getattr(dataset, y_name)[idx])
if which_set == "train":
dataset.label_mask = dataset.label_mask[idx]
dataset_x = getattr(dataset, x_name)
dataset_y = getattr(dataset, y_name)
for ii in range(0, num_examples, batch_size):
x = []
y = []
for j in range(batch_size):
x.append(self.scaler(getImage(dataset_x.iloc[(ii+j)%num_examples])))
y.append(dataset_y.iloc[(ii+j)%num_examples])
x = np.array(x).reshape(-1, 600, 800, 3)
y = np.array(y).reshape(-1, 1)
if which_set == "train":
# When we use the data for training, we need to include
# the label mask, so we can pretend we don't have access
# to some of the labels, as an exercise of our semi-supervised
# learning ability
yield x, y, self.label_mask[ii:ii+batch_size]
else:
yield x, y
In [10]:
def model_inputs(real_dim, z_dim):
inputs_real = tf.placeholder(tf.float32, (None, real_dim[0], real_dim[1], real_dim[2]), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
y = tf.placeholder(tf.int32, (None), name='y')
label_mask = tf.placeholder(tf.int32, (None), name='label_mask')
return inputs_real, inputs_z, y, label_mask
In [11]:
def generator(z, output_dim, reuse=False, alpha=0.2, training=True, size_mult=128):
with tf.variable_scope('generator', reuse=reuse):
# First fully connected layer
x1 = tf.layers.dense(z, 4 * 4 * size_mult * 4)
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 4, 4, size_mult * 4))
x1 = tf.layers.batch_normalization(x1, training=training)
x1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d_transpose(x1, size_mult * 2, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=training)
x2 = tf.maximum(alpha * x2, x2)
x3 = tf.layers.conv2d_transpose(x2, size_mult, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=training)
x3 = tf.maximum(alpha * x3, x3)
# Output layer
logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same')
out = tf.tanh(logits)
return out
In [12]:
def discriminator(x, reuse=False, alpha=0.2, drop_rate=0., num_classes=10, size_mult=64):
with tf.variable_scope('discriminator', reuse=reuse):
x = tf.layers.dropout(x, rate=drop_rate/2.5)
# Input layer is 32x32x3
x1 = tf.layers.conv2d(x, size_mult, 3, strides=2, padding='same')
relu1 = tf.maximum(alpha * x1, x1)
relu1 = tf.layers.dropout(relu1, rate=drop_rate)
x2 = tf.layers.conv2d(relu1, size_mult, 3, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * x2, x2)
x3 = tf.layers.conv2d(relu2, size_mult, 3, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
relu3 = tf.layers.dropout(relu3, rate=drop_rate)
x4 = tf.layers.conv2d(relu3, 2 * size_mult, 3, strides=1, padding='same')
bn4 = tf.layers.batch_normalization(x4, training=True)
relu4 = tf.maximum(alpha * bn4, bn4)
x5 = tf.layers.conv2d(relu4, 2 * size_mult, 3, strides=1, padding='same')
bn5 = tf.layers.batch_normalization(x5, training=True)
relu5 = tf.maximum(alpha * bn5, bn5)
x6 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=2, padding='same')
bn6 = tf.layers.batch_normalization(x6, training=True)
relu6 = tf.maximum(alpha * bn6, bn6)
relu6 = tf.layers.dropout(relu6, rate=drop_rate)
x7 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=1, padding='valid')
# Don't use bn on this layer, because bn would set the mean of each feature
# to the bn mu parameter.
# This layer is used for the feature matching loss, which only works if
# the means can be different when the discriminator is run on the data than
# when the discriminator is run on the generator samples.
relu7 = tf.maximum(alpha * x7, x7)
# Flatten it by global average pooling
features = tf.reduce_mean(relu7, (1, 2))
# Set class_logits to be the inputs to a softmax distribution over the different classes
class_logits = tf.layers.dense(features, num_classes + extra_class)
# Set gan_logits such that P(input is real | input) = sigmoid(gan_logits).
# Keep in mind that class_logits gives you the probability distribution over all the real
# classes and the fake class. You need to work out how to transform this multiclass softmax
# distribution into a binary real-vs-fake decision that can be described with a sigmoid.
# Numerical stability is very important.
# You'll probably need to use this numerical stability trick:
# log sum_i exp a_i = m + log sum_i exp(a_i - m).
# This is numerically stable when m = max_i a_i.
# (It helps to think about what goes wrong when...
# 1. One value of a_i is very large
# 2. All the values of a_i are very negative
# This trick and this value of m fix both those cases, but the naive implementation and
# other values of m encounter various problems)
if extra_class:
real_class_logits, fake_class_logits = tf.split(class_logits, [num_classes, 1], 1)
assert fake_class_logits.get_shape()[1] == 1, fake_class_logits.get_shape()
fake_class_logits = tf.squeeze(fake_class_logits)
else:
real_class_logits = class_logits
fake_class_logits = 0.
mx = tf.reduce_max(real_class_logits, 1, keep_dims=True)
stable_real_class_logits = real_class_logits - mx
gan_logits = tf.log(tf.reduce_sum(tf.exp(stable_real_class_logits), 1)) + tf.squeeze(mx) - fake_class_logits
out = tf.nn.softmax(class_logits)
return out, class_logits, gan_logits, features
In [13]:
def model_loss(input_real, input_z, output_dim, y, num_classes, label_mask, alpha=0.2, drop_rate=0.):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param output_dim: The number of channels in the output image
:param y: Integer class labels
:param num_classes: The number of classes
:param alpha: The slope of the left half of leaky ReLU activation
:param drop_rate: The probability of dropping a hidden unit
:return: A tuple of (discriminator loss, generator loss)
"""
# These numbers multiply the size of each layer of the generator and the discriminator,
# respectively. You can reduce them to run your code faster for debugging purposes.
g_size_mult = 32
d_size_mult = 64
# Here we run the generator and the discriminator
g_model = generator(input_z, output_dim, alpha=alpha, size_mult=g_size_mult)
d_on_data = discriminator(input_real, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult)
d_model_real, class_logits_on_data, gan_logits_on_data, data_features = d_on_data
d_on_samples = discriminator(g_model, reuse=True, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult)
d_model_fake, class_logits_on_samples, gan_logits_on_samples, sample_features = d_on_samples
# Here we compute `d_loss`, the loss for the discriminator.
# This should combine two different losses:
# 1. The loss for the GAN problem, where we minimize the cross-entropy for the binary
# real-vs-fake classification problem.
# 2. The loss for the SVHN digit classification problem, where we minimize the cross-entropy
# for the multi-class softmax. For this one we use the labels. Don't forget to ignore
# use `label_mask` to ignore the examples that we are pretending are unlabeled for the
# semi-supervised learning problem.
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=gan_logits_on_data,
labels=tf.ones_like(gan_logits_on_data)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=gan_logits_on_samples,
labels=tf.zeros_like(gan_logits_on_samples)))
y = tf.squeeze(y)
class_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=class_logits_on_data,
labels=tf.one_hot(y, num_classes + extra_class,
dtype=tf.float32))
class_cross_entropy = tf.squeeze(class_cross_entropy)
label_mask = tf.squeeze(tf.to_float(label_mask))
d_loss_class = tf.reduce_sum(label_mask * class_cross_entropy) / tf.maximum(1., tf.reduce_sum(label_mask))
d_loss = d_loss_class + d_loss_real + d_loss_fake
# Here we set `g_loss` to the "feature matching" loss invented by Tim Salimans at OpenAI.
# This loss consists of minimizing the absolute difference between the expected features
# on the data and the expected features on the generated samples.
# This loss works better for semi-supervised learning than the tradition GAN losses.
data_moments = tf.reduce_mean(data_features, axis=0)
sample_moments = tf.reduce_mean(sample_features, axis=0)
g_loss = tf.reduce_mean(tf.abs(data_moments - sample_moments))
pred_class = tf.cast(tf.argmax(class_logits_on_data, 1), tf.int32, name="predict")
eq = tf.equal(tf.squeeze(y), pred_class)
correct = tf.reduce_sum(tf.to_float(eq))
masked_correct = tf.reduce_sum(label_mask * tf.to_float(eq))
return d_loss, g_loss, correct, masked_correct, g_model
In [14]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get weights and biases to update. Get them separately for the discriminator and the generator
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
for t in t_vars:
assert t in d_vars or t in g_vars
# Minimize both players' costs simultaneously
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
shrink_lr = tf.assign(learning_rate, learning_rate * 0.9)
return d_train_opt, g_train_opt, shrink_lr
In [15]:
class GAN:
"""
A GAN model.
:param real_size: The shape of the real data.
:param z_size: The number of entries in the z code vector.
:param learnin_rate: The learning rate to use for Adam.
:param num_classes: The number of classes to recognize.
:param alpha: The slope of the left half of the leaky ReLU activation
:param beta1: The beta1 parameter for Adam.
"""
def __init__(self, real_size, z_size, learning_rate, num_classes=10, alpha=0.2, beta1=0.5):
tf.reset_default_graph()
self.learning_rate = tf.Variable(learning_rate, trainable=False)
self.input_real, self.input_z, self.y, self.label_mask = model_inputs(real_size, z_size)
self.drop_rate = tf.placeholder_with_default(.5, (), "drop_rate")
loss_results = model_loss(self.input_real, self.input_z,
real_size[2], self.y, num_classes, label_mask=self.label_mask,
alpha=0.2,
drop_rate=self.drop_rate)
self.d_loss, self.g_loss, self.correct, self.masked_correct, self.samples = loss_results
self.d_opt, self.g_opt, self.shrink_lr = model_opt(self.d_loss, self.g_loss, self.learning_rate, beta1)
In [16]:
def view_samples(epoch, samples, nrows, ncols, figsize=(8,6)):
fig, axes = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols,
sharey=True, sharex=True)
for ax, img in zip(axes.flatten(), samples[epoch]):
ax.axis('off')
img = ((img - img.min())*255 / (img.max() - img.min())).astype(np.uint8)
ax.set_adjustable('box-forced')
im = ax.imshow(img)
plt.subplots_adjust(wspace=0, hspace=0)
return fig, axes
In [17]:
def train(net, dataset, epochs, batch_size, figsize=(8,6)):
saver = tf.train.Saver()
sample_z = np.random.normal(0, 1, size=(50, z_size))
samples, train_accuracies, test_accuracies = [], [], []
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for e in range(epochs):
print "Epoch",e
t1e = time.time()
num_examples = 0
num_correct = 0
for x, y, label_mask in dataset.batches(batch_size):
assert 'int' in str(y.dtype)
steps += 1
num_examples += label_mask.sum()
# Sample random noise for G
batch_z = np.random.normal(0, 1, size=(batch_size, z_size))
# Run optimizers
t1 = time.time()
_, _, correct = sess.run([net.d_opt, net.g_opt, net.masked_correct],
feed_dict={net.input_real: x, net.input_z: batch_z,
net.y : y, net.label_mask : label_mask})
t2 = time.time()
num_correct += correct
sess.run([net.shrink_lr])
train_accuracy = num_correct / float(num_examples)
print "\t\tClassifier train accuracy: ", train_accuracy
num_examples = 0
num_correct = 0
for x, y in dataset.batches(batch_size, which_set="test"):
assert 'int' in str(y.dtype)
num_examples += x.shape[0]
correct, = sess.run([net.correct], feed_dict={net.input_real: x,
net.y : y,
net.drop_rate: 0.})
num_correct += correct
test_accuracy = num_correct / float(num_examples)
print "\t\tClassifier test accuracy: ", test_accuracy
print "\t\tStep time: ", t2 - t1
t2e = time.time()
print "\t\tEpoch time: ", t2e - t1e
gen_samples = sess.run(
net.samples,
feed_dict={net.input_z: sample_z})
samples.append(gen_samples)
_ = view_samples(-1, samples, 5, 10, figsize=figsize)
plt.show()
# Save history of accuracies to view after training
train_accuracies.append(train_accuracy)
test_accuracies.append(test_accuracy)
saver.save(sess, './checkpoints/generator.ckpt')
with open('samples.pkl', 'wb') as f:
pkl.dump(samples, f)
return train_accuracies, test_accuracies, samples
In [18]:
#!mkdir checkpoints
In [19]:
extra_class = 0
real_size = (600,800,3)
z_size = 100
learning_rate = 0.01
net = GAN(real_size, z_size, learning_rate)
In [20]:
dataset = Dataset(trainset, testset)
batch_size = 2
epochs = 50
train_accuracies, test_accuracies, samples = train(net, dataset, epochs, batch_size, figsize=(10,5))
In [21]:
fig, ax = plt.subplots()
plt.plot(train_accuracies, label='Train', alpha=0.5)
plt.plot(test_accuracies, label='Test', alpha=0.5)
plt.title("Accuracy")
plt.legend()
Out[21]:
In [22]:
_ = view_samples(-1, samples, 5, 10, figsize=(10,5))
Making Predictions¶
In [23]:
# prediction function
label = ['red', 'yellow', 'green', '', 'unknown']
import os
def prediction(image_file):
# load color (BGR) image
img = cv2.imread(image_file)
# convert BGR image to RGB for plotting
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
sess = tf.Session()
saver = tf.train.import_meta_graph('./checkpoints/generator.ckpt.meta')
saver.restore(sess,tf.train.latest_checkpoint('./checkpoints/'))
graph = tf.get_default_graph()
input_real = graph.get_tensor_by_name("input_real:0")
drop_rate = graph.get_tensor_by_name("drop_rate:0")
predict = graph.get_tensor_by_name("predict:0")
predict = sess.run(predict, feed_dict = {
input_real: scale(img.reshape(-1, 600, 800, 3)),
drop_rate:0.})
# display the image and prediction
fg, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
ax1.text(.5, .5, 'Most Likely: %s'%(label[predict[0]]), fontsize=12, horizontalalignment='center', verticalalignment='center')
ax1.set_axis_off()
ax2.set_title('Prediction')
ax2.imshow(img)
plt.show()
In [24]:
prediction('../../data/collections/samples/session1_100.jpg')
In [25]:
prediction('../../data/collections/samples/session1_77.jpg')
In [26]:
prediction('../../data/collections/samples/session1_80.jpg')
In [27]:
prediction('../../data/collections/samples/session1_271.jpg')
In [ ]: